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Summer SchoolIntroduction to Business Data Science with Python

Tuition fee €2,150 per programme

The program price consists of the course/tuition fee (student or working professional, see details below) plus the registration fee (€60).

Student course/tuition fee: €2090
Working professional course/tuition fee: €2510

This course/tuition fee covers the course, course materials and a cultural program.

Registration fee €60 one-time

The registration fee is in addition to the course/tuition fee and covers the processing of your application. It is payable upon registration. Please note that the registration fee is non-refundable.

More information

tu.berlin/..iness-data-science-with-python 

Overview

The course of Business Data Science with Python contains the learning materials, practices and case studies to develop the knowledge and skills of the students in the field of data science and its application in the real business/work world.

The students will learn how to apply analytical techniques and scientific principles to extract valuable information from business data for decision-making, strategic planning.

This skills-based specialization is intended for learners who have a basic programming background and want to apply statistical, machine learning, information visualization, and data analysis techniques through python programming language and other tools.

Learning Goals:

  • Understanding statistical association and the difference between causation and correlation
  • Understanding and developing the skills to apply descriptive techniques and Statistical inference in the real business cases, social and marketing studies
  • Structural Equation Modeling SEM, Confirmatory Factor analysis CFA , Path analysis
  • Time-series Analysis
  • Advanced visualization techniques as an initial step to solve data analysis problems, including Geo-based visualization and Network visualization
  • Machine Learning (ML) process, supervised vs unsupervised, validation approaches, over/ under fitting
  • Introduction to basic Clustering approaches
  • Introduction to basic Classification approaches
  • Introduction to Social Network concept and its principles and applications.

Main course components:

Principles of Python

  • Descriptive techniques
  • Statistical inference
  • Linear Regression
  • Nonlinear Regression
  • Structural Equation Modeling SEM
  • Time series
  • Clustering
  • Classification
Apply now! Summer Term 2 2024
Application period has ended, but late applications can be submitted until the commence date
Studies commence
15 Jul 2024
Apply now! Summer Term 2 2024
Application period has ended, but late applications can be submitted until the commence date
Studies commence
15 Jul 2024